from libreyolo import LibreYOLO9

model = LibreYOLO9("LibreYOLO9t-cls.pt", task="classify")
result = model.predict("image.jpg")
print(result)  # top-1 class and confidence

Training

Dataset: imagenette160 (train: 9,469 images · val: 3,925 images · 10 classes)
Epochs: 30
Optimizer: AdamW
Scheduler: Warm cosine (5 warmup epochs)
Augmentation: RandomResizedCrop + TrivialAugmentWide + RandomErasing
Transfer: pretrained backbone (COCO detection weights)

Limitations

These weights are trained on imagenette (10 classes, ~9.5k images), a fast.ai benchmark subset of ImageNet. They serve as a functional demo of the classification pipeline. For production use we recommend fine-tuning on your own dataset. A full ImageNet-1k training run is planned when compute budget allows.

License

MIT — see LICENSE. The imagenette dataset is derived from ImageNet; the original ImageNet terms apply to the training data.

LibreYOLO/LibreYOLO9t-cls

LibreYOLO9t-cls

Model Details

Classes

Usage

Training

Limitations

License

LibreYOLO/
LibreYOLO9t-cls